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Learn the OpenAI API with Python and NLP by mastering API endpoints, text generation, moderation, embeddings, and Flask integration for real-world tasks.

Using OpenAI API for Natural Language Processing in Python.png

dockerfile.tar.gz

openai_completion

embeddings

python

flask

This course introduces OpenAI API and NLP in Python, teaching you how to use the OpenAI API for real-world natural language processing tasks. You’ll begin by exploring the OpenAI API with Python, setting up your account, and learning how to access key endpoints. The completions endpoint will be covered for generating, classifying, and transforming text.

The course then covers advanced NLP in Python techniques using other OpenAI endpoints like moderations and embeddings for in-depth text analysis. You’ll practice these methods to efficiently analyze and manipulate text data.

Finally, you will integrate your OpenAI skills with Flask to build interactive, NLP-powered applications. The course emphasizes real-world projects, helping you use OpenAI’s capabilities to solve problems in content generation, text classification, and more. After completing this course, you can apply NLP in Python using OpenAI API to create scalable solutions.

Using OpenAI API for Natural Language Processing in Python

# Overview

**Classification** is the process of categorizing the data into groups. In this lesson, we’ll focus on the text classification problem. There are many applications of text classification, including sentiment analysis, natural language inference (NLI), and grammatical verification. We’ll use the chat completions endpoint of OpenAI to classify the text data, which can be used to label any text without fine-tuning the model. To classify the text, we provide the task description and some optional examples with labels.




# Single tweet classifier
The input parameters are the same as we saw in the previous lesson. Let’s see how to provide a prompt to the API for classification:

```text
Classify the Tweet's label as positive, neutral, or negative.

Tweet: I loved the new Spiderman movie!
Sentiment:
```

The first sentence of the prompt is the description of what type of task to perform. We can also provide labels here to get the specific outputs, like we have provided the labels `positive`, `neutral` and `negative` in the prompt. Then, in the prompt, we use simple terms for the input and the required output, like `Tweet` and `Sentiment,` respectively. If there is a new thing that the API is unfamiliar with, we have to provide examples in such cases.


## Examples

Let’s try the prompt above in the code widget below:

Learn how to perform classification tasks using the completions endpoint.

Introduction

The Completions Endpoint

Miscellaneous Endpoints

APIs Integration in Flask

Conclusion

Appendix

Exploring OpenAI API

Classification

Overview

Single tweet classifier